We remark that this approach is inspired by the neighborhood
aggregation approaches outlined in graph kernel literature in the area
of machine learning, especially the Weisfeiler-Lehman (WL)
kernel [1]}.
An overarching principle in the design of
graph kernels is the representation and comparison of local structure in
graphs. Two vertices are considered similar if their
neighborhoods are colored / labeled similarly. A natural extension to
this notion is that two graphs are considered similar if they are
composed of vertices with similar neighborhoods, i.e., they have a
similar local structure.
